What is Deep Learning and Why is it Valuable? (Beginner Guide)

In this article you will read more about what deep learning is and why it is valuable to businesses across industries. Particularly, you will learn about:

  1. The history of the technology
  2. What is deep learning? Definition of the term and what it includes
  3. Why it is important for businesses
  4. Examples of real-world applications

The History of Deep Learning

Deep learning, a sector of artificial intelligence, has been applied in various fields since the early 2000’s, but has gained traction in the last ten years as a valuable commodity for tech industries. The technology allows companies to recommend users material they would be personally interested in (a feature implemented in platforms such as Facebook and Google), and perform tasks such as automated driving at Tesla and auto-captioning on YouTube.

Definition of Deep Learning

The dictionary definition of deep learning is a sector of machine learning methods based on artificial neural networks with representation learning.

But what does this mean? Deep learning is used by programmers to teach computers to do what humans have been doing since the beginning of time: learning by example – or receiving, processing, and filtering complex information with all five senses to produce a final output. The models train off layered algorithms in order to achieve a specific goal.

This image shows the hierarchy of artificial intelligence, which encompasses machine learning, deep learning, and computer vision.
Classification of Computer Vision and How it Relates to Deep Learning

How Deep Learning Works

Deep learning models rely on layers of artificial neural networks (rather than inputted data) to train from programmed instances of features or distinctions. These multilevel layers allow models to detect and train from its own mistakes. Within the hierarchy of programmed algorithms, each contains its own concept for the model to search for, allowing it to validate its own outputs.

A machine learning model, on the other hand, would produce errors or low accuracy rates when the given structured data is not sufficient. However, deep learning models do produce wrong classifications when the algorithms they train off of do not specify data clearly enough.

The Value Is at an All-Time High

Deep learning, due to its ease of implementation and knack for efficiently solving problems, is becoming increasingly valuable to companies. Given its custom attributes, the algorithms behind it are worth a lot today. Creating an algorithm that can solve a distinct, new problem boosts the value of a product that incorporates it. Because of the uniqueness of novel algorithms, companies that create them often generate massive profits.

For example, Facebook had 0 deep or machine learning patents in 2010, while just six years later in 2016, this number shot up to 55. Facebook now utilizes artificial intelligence learning for features such as its custom news source algorithms, which show users news stories and posts that pertain to their needs and views.

As more businesses recognize the significance of the technology, their profits and value surge. Bill Gates has been quoted to have said the following:

A breakthrough in machine learning is worth 10 Microsofts. Bill Gates

This further exemplifies the desirability of artificial intelligence in tech companies today.

What’s Next?

Deep learning is in action all around us in everyday life, from the personalized feed curated by Facebook in the morning, to the cars we drive home at night. It continues to fuel the innovation and expansion of tech companies.

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